Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyan...Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. The results obtained by artificial neural network give advanced regression models with good prediction ability. The two optimal artificial neural network models obtained have coefficients of determination of 0.746 and 0.756. The lowest prediction’s root mean square error obtained is 0.607. Artificial neural networks provide improved models for heterogeneous data sets without splitting them into families. Both the external and cross-validation methods are used to validate the performances of the resulting models. Randomization test is employed to check the suitability of the models.展开更多
Based on molecular topological chemical theory,as well as the structural characteristic and the valence connection bonding atom i,we introduced structure information index mE(m=0,1).Combing with electronic structure p...Based on molecular topological chemical theory,as well as the structural characteristic and the valence connection bonding atom i,we introduced structure information index mE(m=0,1).Combing with electronic structure parameter of chlorophenols,the new method with ANN analysis was used to predict the biology-toxicity parameters of chlorophenols.The prediction result was more satisfied than others analysis.展开更多
O-ethyl-O-aryl-N-isopropyl-phosphoramidothioates have relatively high herbicidal ac-tivity. The exact and comprehensive QSAN study is the key to finding new compoundswith high activity. Artificial neural networks (ANN...O-ethyl-O-aryl-N-isopropyl-phosphoramidothioates have relatively high herbicidal ac-tivity. The exact and comprehensive QSAN study is the key to finding new compoundswith high activity. Artificial neural networks (ANNs) are a newly emerging field ofinformation processing technology. As ANNs can be taught complex nonlinearinput-output transformations and have the ability of adaptive learning, resistance tonoise and fault tolerance, they can solve the pattern recognition and funtionalmapping problems.展开更多
Support vector machine (SVM), partial least squares (PLS), and Back-Propagation artificial neural net- work (ANN) were employed to establish QSAR models of 2 dipeptide datasets. In order to validate predictive capabil...Support vector machine (SVM), partial least squares (PLS), and Back-Propagation artificial neural net- work (ANN) were employed to establish QSAR models of 2 dipeptide datasets. In order to validate predictive capabilities on external dataset of the resulting models, both internal and external validations were performed. The division of dataset into both training and test sets was carried out by D-optimal design. The results showed that support vector machine (SVM) behaved well in both calibration and prediction. For the dataset of 48 bitter tasting dipeptides (BTD), the results obtained by support vector regression (SVR) were superior to that by PLS in both calibration and prediction. When compared with BP artificial neural network, SVR showed less calibration power but more predictive capability. For the dataset of angiotensin-converting enzyme (ACE) inhibitors, the results obtained by support vector machine (SVM) re- gression were equivalent to those by PLS and BP artificial neural network. In both datasets, SVR using linear kernel function behaved well as that using radial basis kernel func- tion. The results showed that there is wide prospect for the application of support vector machine (SVM) into QSAR modeling.展开更多
A Quantitative Structure-activity Relationship（QSAR） model was developed to predict the hallucinogenic activity of phenylalkylamines by Artificial Neural Network（ANN） method.Each compound was represented by the ca...A Quantitative Structure-activity Relationship（QSAR） model was developed to predict the hallucinogenic activity of phenylalkylamines by Artificial Neural Network（ANN） method.Each compound was represented by the calculated structural descriptors involving constitutional,topological,geometrical,electrostatic and quantum-chemical features of compound.The ANN method produced a nonlinear and seven-descriptor QSAR model with a standard error S = 0.0128 and a correlation coefficient R = 0.9752.The electronic properties of 75 phenylalkylamines were calculated with Gaussian 03 program at the DFT/B3LYP/6-311＋G（d,p） level.The quantum chemical analyses were performed from two aspects of frontier molecular orbital and charge distribution.The results show that seven structural describers are crucial to the hallucinogenic activity of phenylalkylamines and that the para-and ortho-positions could be active sites acting as electron donors.展开更多
This work was carried out on a series of twenty-two (22) benzimidazole derivatives with inhibitory activities against Mycobacterium tuberculosis H37Rv by applying the Quantitative Structure-Activity Relationship (QSAR...This work was carried out on a series of twenty-two (22) benzimidazole derivatives with inhibitory activities against Mycobacterium tuberculosis H37Rv by applying the Quantitative Structure-Activity Relationship (QSAR) method. The molecules were optimized at the level DFT/B3LYP/6-31 + G (d, p), to obtain the molecular descriptors. We used three statistical learning tools namely, the linear multiple regression (LMR) method, the nonlinear regression (NLMR) and the artificial neural network (ANN) method. These methods allowed us to obtain three (3) quantitative models from the quantum descriptors that are, chemical potential (μ), polarizability (α), bond length l (C = N), and lipophilicity. These models showed good statistical performance. Among these, the ANN has a significantly better predictive ability R<sup>2</sup> = 0.9995;RMSE = 0.0149;F = 31879.0548. The external validation tests verify all the criteria of Tropsha et al. and Roy et al. Also, the internal validation tests show that the model has a very satisfactory internal predictive character and can be considered as robust. Moreover, the applicability range of this model determined from the levers shows that a prediction of the pMIC of the new benzimidazole derivatives is acceptable when its lever value is lower than 1.展开更多
文摘Quantitative structure–activity relationship study using artificial neural network (ANN) methodology were conducted to predict the inhibition constants of 127 symmetrical and unsymmetrical cyclic urea and cyclic cyanoguanidine derivatives containing different substituent groups such as: benzyl, isopropyl, 4-hydroxybenzyl, ketone, oxime, pyrazole, imidazole, triazole and having anti-HIV-1 protease activities. The results obtained by artificial neural network give advanced regression models with good prediction ability. The two optimal artificial neural network models obtained have coefficients of determination of 0.746 and 0.756. The lowest prediction’s root mean square error obtained is 0.607. Artificial neural networks provide improved models for heterogeneous data sets without splitting them into families. Both the external and cross-validation methods are used to validate the performances of the resulting models. Randomization test is employed to check the suitability of the models.
文摘Based on molecular topological chemical theory,as well as the structural characteristic and the valence connection bonding atom i,we introduced structure information index mE(m=0,1).Combing with electronic structure parameter of chlorophenols,the new method with ANN analysis was used to predict the biology-toxicity parameters of chlorophenols.The prediction result was more satisfied than others analysis.
文摘O-ethyl-O-aryl-N-isopropyl-phosphoramidothioates have relatively high herbicidal ac-tivity. The exact and comprehensive QSAN study is the key to finding new compoundswith high activity. Artificial neural networks (ANNs) are a newly emerging field ofinformation processing technology. As ANNs can be taught complex nonlinearinput-output transformations and have the ability of adaptive learning, resistance tonoise and fault tolerance, they can solve the pattern recognition and funtionalmapping problems.
基金This work was supported by the Fok-Yingtung Educational Foundation(FYEF)(Grant No.98-7-6)the National Chun-hui Project Foundation(NCPF)(Grant No.99-04+99-37)Chongqing Applied Fundamental Science Fund(CAFS)(Grant No.01-3-6).
文摘Support vector machine (SVM), partial least squares (PLS), and Back-Propagation artificial neural net- work (ANN) were employed to establish QSAR models of 2 dipeptide datasets. In order to validate predictive capabilities on external dataset of the resulting models, both internal and external validations were performed. The division of dataset into both training and test sets was carried out by D-optimal design. The results showed that support vector machine (SVM) behaved well in both calibration and prediction. For the dataset of 48 bitter tasting dipeptides (BTD), the results obtained by support vector regression (SVR) were superior to that by PLS in both calibration and prediction. When compared with BP artificial neural network, SVR showed less calibration power but more predictive capability. For the dataset of angiotensin-converting enzyme (ACE) inhibitors, the results obtained by support vector machine (SVM) re- gression were equivalent to those by PLS and BP artificial neural network. In both datasets, SVR using linear kernel function behaved well as that using radial basis kernel func- tion. The results showed that there is wide prospect for the application of support vector machine (SVM) into QSAR modeling.
基金supported by the Natural Science Foundation of Shanxi Province (2007011025)
文摘A Quantitative Structure-activity Relationship（QSAR） model was developed to predict the hallucinogenic activity of phenylalkylamines by Artificial Neural Network（ANN） method.Each compound was represented by the calculated structural descriptors involving constitutional,topological,geometrical,electrostatic and quantum-chemical features of compound.The ANN method produced a nonlinear and seven-descriptor QSAR model with a standard error S = 0.0128 and a correlation coefficient R = 0.9752.The electronic properties of 75 phenylalkylamines were calculated with Gaussian 03 program at the DFT/B3LYP/6-311＋G（d,p） level.The quantum chemical analyses were performed from two aspects of frontier molecular orbital and charge distribution.The results show that seven structural describers are crucial to the hallucinogenic activity of phenylalkylamines and that the para-and ortho-positions could be active sites acting as electron donors.
文摘This work was carried out on a series of twenty-two (22) benzimidazole derivatives with inhibitory activities against Mycobacterium tuberculosis H37Rv by applying the Quantitative Structure-Activity Relationship (QSAR) method. The molecules were optimized at the level DFT/B3LYP/6-31 + G (d, p), to obtain the molecular descriptors. We used three statistical learning tools namely, the linear multiple regression (LMR) method, the nonlinear regression (NLMR) and the artificial neural network (ANN) method. These methods allowed us to obtain three (3) quantitative models from the quantum descriptors that are, chemical potential (μ), polarizability (α), bond length l (C = N), and lipophilicity. These models showed good statistical performance. Among these, the ANN has a significantly better predictive ability R<sup>2</sup> = 0.9995;RMSE = 0.0149;F = 31879.0548. The external validation tests verify all the criteria of Tropsha et al. and Roy et al. Also, the internal validation tests show that the model has a very satisfactory internal predictive character and can be considered as robust. Moreover, the applicability range of this model determined from the levers shows that a prediction of the pMIC of the new benzimidazole derivatives is acceptable when its lever value is lower than 1.
